title: Labeling colorectal NBI zoom-videoendoscope image sequences with MRF and SVM.
abstract: In this paper, we propose a sequence labeling method by using SVM posterior probabilities with a Markov Random Field (MRF) model for colorectal Narrow Band Imaging (NBI) zoom-videoendoscope. Classifying each frame of a video sequence by SVM classifiers independently leads to an output sequence which is unstable and hard to understand by endoscopists. To make it more stable and readable, we use an MRF model to label the sequence of posterior probabilities. In addition, we introduce class asymmetry for the NBI images in order to keep and enhance frames where there is a possibility that cancers might have been detected. Experimental results with NBI video sequences demonstrate that the proposed MRF model with class asymmetry performs much better than a model without asymmetry.
authors.forename: Tsubasa
authors.initials: T
authors.lastname: Hirakawa
authors.forename: Toru
authors.initials: T
authors.lastname: Tamaki
authors.forename: Bisser
authors.initials: B
authors.lastname: Raytchev
authors.forename: Kazufumi
authors.initials: K
authors.lastname: Kaneda
authors.forename: Tetsushi
authors.initials: T
authors.lastname: Koide
authors.forename: Shigeto
authors.initials: S
authors.lastname: Yoshida
authors.forename: Yoko
authors.initials: Y
authors.lastname: Kominami
authors.forename: Taiji
authors.initials: T
authors.lastname: Matsuo
authors.forename: Rie
authors.initials: R
authors.lastname: Miyaki
authors.forename: Shinji
authors.initials: S
authors.lastname: Tanaka
mesh_terms: D053704:Capsule Endoscopy; D015179:Colorectal Neoplasms / Q000175:diagnosis*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008390:Markov Chains; D062048:Narrow Band Imaging / Q000379:methods*; D060388:Support Vector Machine